Traditional calibration techniques for engines are based upon steady-state data collection on an operating engine, over a range of actuator settings and operating conditions. The steady state data is used with an optimization methodology to arrive at optimal or near optimal actuator settings to achieve goals for engine emissions, performance, and fuel economy.
The effectiveness of the traditional technique is high, in that optimal actuator settings can be found. However, the time required for the data collection and optimization process is too long. Considerable time is consumed by the data collection process because the engine must stabilize at each test point until steady state operation is achieved.
For situations in which true steady state is unachievable or requires too much time, transient data may be acquired, with the assumption that the persistent transient behavior of the engine will not appreciably affect the final steady state calibration. Of course, for highly transient engine operation, this assumption leads to poor calibration results.
Advanced methods for selection and use of “fractional” experimental data sets for use in the optimization process generally fall into the category of statistical design of experiments. The key to the method is in selection of experimental data points that will be most useful, while elimination of less useful data points, during acquisition, is made. Hence, the eliminated data is not available for later use. The time savings accomplished with these methods is a strong function of the numbers of experimental points not tested. A tradeoff exists in time-savings versus the ability to arrive at optimal actuator settings with a fractionated experimental data set.